With the advent of an aging welfare society, the importance of home health monitoring for disease prevention and self care is expected to continue to grow, and various bioinstrumentation methods have been proposed. An aged but healthy normal person who can live an ordinary life can be mentioned as an example of a person to be the subject of such home health monitoring. However, under the present circumstances, such a person lacks the motivation to exercise health control and perform a troublesome device operation, because he/she does not need imminent health care. Therefore, a proposal has been made of various bioinstrumentation devices capable of measuring biomedical information under an unconscious condition without any device operation. As measurement methods employed in such bioinstrumentation devices, there are known a method of arranging temperature sensors in a bed or a bottom mattress and, from a temperature distribution, recording body movements at the time of getting into bed, at the time of rising from bed, and during sleep in bed, a method of deriving an electrocardiogram from an electrically conductive sheet and a pillow cover, and a method of measuring respiration and heart beat during sleep by use of a load sensor such as a highly accurate strain gauge or a load cell.
Recently, a proposal has been made to use such bioinstrumentation technology as a security device applied in the safety confirmation of an aged person besides the usage of health monitoring. For example, a leaving-bed sensor has been generally sold for detecting the leaving or falling out of bed of a person during sleep by measuring an electrostatic capacity etc. Additionally, an apnea detector is well known in which an apnea state, caused by an apnea syndrome by which breathing ceases during sleep, is detected by measuring respiration with a pressure-sensitive sensor, and a third party can be informed of the state (e.g., Japanese publication of unexamined patent application No. (hereinafter referred to as JP-A-) 2000-107154).
As a matter of course, respiration provides a very useful clue for immediately knowing the health condition of a person. Respiration measurement performed during sleep is expected to be useful not only in detecting an apnea syndrome but also in discovering a spasmodic incident caused by a sudden attack during sleep. As mentioned above, a typical respiration detecting method during sleep is a method of detecting respiration based on time base measured values of a lead sensor or a pressure-sensitive sensor. Additionally, a method of using a vibration sensor, a radio-wave sensor, or air-pressure detection is publicly known (e.g., JP-A-H7-327939, JP-A-H11-28195, and JP-A-2000-83927). In these methods, since a measured signal is weak, a high-performance signal amplifier or some kind of signal processing is required to acquire and detect a stable signal, and, as a system, it becomes expensive and large in scale.
On the other hand, some proposals have been also made of a method of acquiring an image of a sleeping person by use of an image pickup device and detecting respiration based on the acquired image. With recent developments in electronic equipment, a high-performance image pickup device has appeared on the market at an extremely low price, and, since the device has noncontact properties, the method of detecting respiration based on an image has been brought to public attention as a technique having high practical usefulness.
For example, in “Image-processing device and patient-observing device” of JP-A-H11-86002 and “Region-of-interest setting device of respiration monitoring and respiration-monitoring system” of JP-A-H11-225997, the basic features of those inventions is to monitor respiration by examining a difference between images acquired in different time by the image pickup device.
The “image-processing device and patient-observing device” of JP-A-H11-86002 is composed of a TV camera, a respiration monitoring device, and a local-region automatic setting processing device. The local-region automatic setting processing device is composed of an edge detecting section that detects an edge included in a local region image set on an image for which a patient who requires care is photographed by the TV camera, a brightness distribution measuring section that measures the brightness distribution of each local partial region image divided by the edge in the local region image, and a determining section that sets a local region image to extract movement information by analyzing information about a detected edge and information about a measured brightness distribution. The local region image is divided into a plurality of local partial regions that are identical in brightness, and a time differentiation process is applied to each pixel included in each local partial region, and the total amount of the time differentiation is calculated. A time base change of this amount is analyzed, and a periodic appearance is detected as respiration, whereas irregularities in the period and amplitude are detected as great body movements, such as a body twist.
In the “Region-of-interest setting device of respiration monitoring and respiration-monitoring system” of JP-A-H11-225997, a calculation is first performed of the absolute value of a difference between every one frame of a plurality of frame images picked up by a CCD camera over ½ periods of respiration. Thereafter, the difference images are integrated and stored, the positions and sizes of variation regions are then calculated from variation information that has been integrated and stored, and they are set as temporary regions in order from the largest to the smallest region in the variation ones. Thereafter, a judgment is made of whether a concentration-value histogram, that shows the distribution of the number of pixels of each concentration value, exhibits a two-peak characteristic having a height greater than a predetermined value in the temporary regions and whether the area value of the variation regions is greater than a given value. If this condition is satisfied, the temporary region is set as a region of interest (abbreviated as ROI). Further, a time differentiation process is performed in the set ROI, the absolute value of the difference between each pixel is then obtained, and a surface integral is applied. The surface integral is performed in a time series manner, and, like the invention of JP-A-H11-86002, a time base change in this surface integral is analyzed, and a periodic appearance is detected as respiration, whereas irregularities in the period and amplitude are detected as great body movements, such as a body twist.
Further, a method of detecting respiration by an optical flow of movement of a sleeping person is known as an image-using technique other than the aforementioned methods. The optical flow is characterized by detecting the movement of a sleeping person as a velocity vector, and a respiration waveform having a periodic rhythm and a body-movement waveform having a high peak can be obtained from a vector field by employing the fact that most upward vectors are detected in inspiration whereas most downward vectors are detected in expiration.
The respective methods described above are to observe the movement of shadows on a quilt by use of illumination light, and there remains the fundamental problem of being sometimes incapable of detecting the movement of shadows depending on lighting conditions, the posture of a sleeping person, or the design of the quilt. Additionally, since an image pickup device must be set close to the sleeping person in order to photograph the shadow on the quilt, the respiration monitoring according to the aforementioned methods is considered to entail a psychologically overpowered feeling when the person to be monitored goes to bed.
Additionally, it is said that the method based on a time differentiation can evaluate a frequency of the movement of a targeted person, but cannot make a quantitative evaluation of the movement thereof. In contrast, in the optical-flow method, the optical flow enables a quantitative evaluation of the movement of a sleeping person, but, in practice, much computation time is needed to calculate the optical flow, and there remains the problem of requiring expensive processing equipment.